Autoregressive Modeling of Physiological Tremor under Microsurgical Conditions

Brian Becker, Harsha Tummala, and Cameron Riviere
30th Annual International IEEE EMBS Conference, August, 2008, pp. 1948-1951.


Download
  • Adobe portable document format (pdf) (1MB)
Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

Abstract
Tremor was recorded under simulated vitreoretinal microsurgical conditions as subjects attempted to hold an instrument motionless. Several autoregressive models (AR, ARMA, multivariate, and nonlinear) are generated to predict the next value of tremor. It is shown that a sixth order ARMA model predictor can predict a tremor having an amplitude of 96.6 ± 84.5 microns RMS with an error of 8.2 ± 5.9 microns RMS, a mean improvement of 47.5% over simple last-value prediction.

Notes
Associated Center(s) / Consortia: Medical Robotics Technology Center
Associated Lab(s) / Group(s): Surgical Mechatronics Laboratory
Associated Project(s): Micron: Intelligent Microsurgical Instruments and ASAP

Text Reference
Brian Becker, Harsha Tummala, and Cameron Riviere, "Autoregressive Modeling of Physiological Tremor under Microsurgical Conditions," 30th Annual International IEEE EMBS Conference, August, 2008, pp. 1948-1951.

BibTeX Reference
@inproceedings{Becker_2008_6447,
   author = "Brian Becker and Harsha Tummala and Cameron Riviere",
   title = "Autoregressive Modeling of Physiological Tremor under Microsurgical Conditions",
   booktitle = "30th Annual International IEEE EMBS Conference",
   pages = "1948-1951",
   month = "August",
   year = "2008",
}